library(pixmap)
library(magrittr)
library(EBImage)
library(image.LineSegmentDetector)
library(image.ContourDetector)
library(image.CannyEdges)
setwd("~/Zellkraftwerk/packages/RJobTissueArea")
file <- file.path("inst/data","data_sum_M1730408_pos11.csv")
data.sum <- file%>%
data.table::fread()
m.data_sum <- data.sum%>%
as.matrix()
#cellres: pixel resolution in horizontal and vertical direction
image <-pixmapGrey(m.data_sum,cellres=c(1,1))
plot(image)
EBImage
constant threshold
grid <- expand.grid(
sigma = c(1,2,3,4,5,10,100),
threshold = seq(0,10,1)
)
grid$min <- NA
grid$max <- NA
img2 <- image@grey * 255
# Set up plots for 96 dpi images
#library(EBImage)
#dm <- dim(img2)/96
#dev.new(width = dm[1], height = dm[2])
# Low pass filter with gblur and make binary
par(mfrow=c(1,2))
for(i in 1:dim(grid)[1]){
writeLines(c(
paste0("- sigma: ",grid$sigma[i]),
paste0("- threshold: ", grid$threshold[i])
))
#Low-pass Gaussian filter
xb <- EBImage::gblur(img2, grid$sigma[i])
xb <- round(xb,digits = 0)
#plot(xb)
image2 <- pixmapGrey(xb,cellres=c(1,1)) #(xb-min(xb))/(max(xb)-min(xb))
plot(image2)
grid$min[i] <- min(xb)
grid$max[i] <- max(xb)
#threshold filtering
pos <- which(xb > grid$threshold[i])
xt <- xb
xt[which(xb > grid$threshold[i])] <- 1
xt[which(xb <= grid$threshold[i])] <- 0
#xt <- EBImage::thresh(xb, w=grid$w[i],
# h=grid$w[i],
# offset = grid$offset[i])
#plot(xt)
image3 <- image
image3@grey <- xt
plot(image3)
}## - sigma: 1
## - threshold: 0
## - sigma: 2
## - threshold: 0
## - sigma: 3
## - threshold: 0
## - sigma: 4
## - threshold: 0
## - sigma: 5
## - threshold: 0
## - sigma: 10
## - threshold: 0
## - sigma: 100
## - threshold: 0
## - sigma: 1
## - threshold: 1
## - sigma: 2
## - threshold: 1
## - sigma: 3
## - threshold: 1
## - sigma: 4
## - threshold: 1
## - sigma: 5
## - threshold: 1
## - sigma: 10
## - threshold: 1
## - sigma: 100
## - threshold: 1
## - sigma: 1
## - threshold: 2
## - sigma: 2
## - threshold: 2
## - sigma: 3
## - threshold: 2
## - sigma: 4
## - threshold: 2
## - sigma: 5
## - threshold: 2
## - sigma: 10
## - threshold: 2
## - sigma: 100
## - threshold: 2
## - sigma: 1
## - threshold: 3
## - sigma: 2
## - threshold: 3
## - sigma: 3
## - threshold: 3
## - sigma: 4
## - threshold: 3
## - sigma: 5
## - threshold: 3
## - sigma: 10
## - threshold: 3
## - sigma: 100
## - threshold: 3
## - sigma: 1
## - threshold: 4
## - sigma: 2
## - threshold: 4
## - sigma: 3
## - threshold: 4
## - sigma: 4
## - threshold: 4
## - sigma: 5
## - threshold: 4
## - sigma: 10
## - threshold: 4
## - sigma: 100
## - threshold: 4
## - sigma: 1
## - threshold: 5
## - sigma: 2
## - threshold: 5
## - sigma: 3
## - threshold: 5
## - sigma: 4
## - threshold: 5
## - sigma: 5
## - threshold: 5
## - sigma: 10
## - threshold: 5
## - sigma: 100
## - threshold: 5
## - sigma: 1
## - threshold: 6
## - sigma: 2
## - threshold: 6
## - sigma: 3
## - threshold: 6
## - sigma: 4
## - threshold: 6
## - sigma: 5
## - threshold: 6
## - sigma: 10
## - threshold: 6
## - sigma: 100
## - threshold: 6
## - sigma: 1
## - threshold: 7
## - sigma: 2
## - threshold: 7
## - sigma: 3
## - threshold: 7
## - sigma: 4
## - threshold: 7
## - sigma: 5
## - threshold: 7
## - sigma: 10
## - threshold: 7
## - sigma: 100
## - threshold: 7
## - sigma: 1
## - threshold: 8
## - sigma: 2
## - threshold: 8
## - sigma: 3
## - threshold: 8
## - sigma: 4
## - threshold: 8
## - sigma: 5
## - threshold: 8
## - sigma: 10
## - threshold: 8
## - sigma: 100
## - threshold: 8
## - sigma: 1
## - threshold: 9
## - sigma: 2
## - threshold: 9
## - sigma: 3
## - threshold: 9
## - sigma: 4
## - threshold: 9
## - sigma: 5
## - threshold: 9
## - sigma: 10
## - threshold: 9
## - sigma: 100
## - threshold: 9
## - sigma: 1
## - threshold: 10
## - sigma: 2
## - threshold: 10
## - sigma: 3
## - threshold: 10
## - sigma: 4
## - threshold: 10
## - sigma: 5
## - threshold: 10
## - sigma: 10
## - threshold: 10
## - sigma: 100
## - threshold: 10
grid## sigma threshold min max
## 1 1 0 0 200
## 2 2 0 0 177
## 3 3 0 0 149
## 4 4 0 0 125
## 5 5 0 0 110
## 6 10 0 0 91
## 7 100 0 2 47
## 8 1 1 0 200
## 9 2 1 0 177
## 10 3 1 0 149
## 11 4 1 0 125
## 12 5 1 0 110
## 13 10 1 0 91
## 14 100 1 2 47
## 15 1 2 0 200
## 16 2 2 0 177
## 17 3 2 0 149
## 18 4 2 0 125
## 19 5 2 0 110
## 20 10 2 0 91
## 21 100 2 2 47
## 22 1 3 0 200
## 23 2 3 0 177
## 24 3 3 0 149
## 25 4 3 0 125
## 26 5 3 0 110
## 27 10 3 0 91
## 28 100 3 2 47
## 29 1 4 0 200
## 30 2 4 0 177
## 31 3 4 0 149
## 32 4 4 0 125
## 33 5 4 0 110
## 34 10 4 0 91
## 35 100 4 2 47
## 36 1 5 0 200
## 37 2 5 0 177
## 38 3 5 0 149
## 39 4 5 0 125
## 40 5 5 0 110
## 41 10 5 0 91
## 42 100 5 2 47
## 43 1 6 0 200
## 44 2 6 0 177
## 45 3 6 0 149
## 46 4 6 0 125
## 47 5 6 0 110
## 48 10 6 0 91
## 49 100 6 2 47
## 50 1 7 0 200
## 51 2 7 0 177
## 52 3 7 0 149
## 53 4 7 0 125
## 54 5 7 0 110
## 55 10 7 0 91
## 56 100 7 2 47
## 57 1 8 0 200
## 58 2 8 0 177
## 59 3 8 0 149
## 60 4 8 0 125
## 61 5 8 0 110
## 62 10 8 0 91
## 63 100 8 2 47
## 64 1 9 0 200
## 65 2 9 0 177
## 66 3 9 0 149
## 67 4 9 0 125
## 68 5 9 0 110
## 69 10 9 0 91
## 70 100 9 2 47
## 71 1 10 0 200
## 72 2 10 0 177
## 73 3 10 0 149
## 74 4 10 0 125
## 75 5 10 0 110
## 76 10 10 0 91
## 77 100 10 2 47adapting threshold
grid <- expand.grid(sigma = c(1,2,3,4,5,10,100),
offset = c(100,10,1,0,0.01,0.001,0.0001,0.00001,0.000001,0.0000001),
w=c(1,5,10,50))
img2 <- image@grey * 255
# Set up plots for 96 dpi images
#library(EBImage)
#dm <- dim(img2)/96
#dev.new(width = dm[1], height = dm[2])
# Low pass filter with gblur and make binary
for(i in 1:dim(grid)[1]){
writeLines(c(
paste0("- sigma: ",grid$sigma[i]),
paste0("- offset: ", grid$offset[i]),
paste0(" - w and h: ",grid$w[i])
))
#Low-pass Gaussian filter
xb <- EBImage::gblur(img2, grid$sigma[i])
plot(xb)
image2 <- pixmapGrey(xb,cellres=c(1,1)) #(xb-min(xb))/(max(xb)-min(xb))
plot(image2)
xt <- EBImage::thresh(xb, w=grid$w[i],
h=grid$w[i],
offset = grid$offset[i])
plot(xt)
image3 <- image
image3@grey <- xt
plot(image3)
}image_line_segment_detector
x <- image@grey * 255
linesegments <- image_line_segment_detector(x,
scale = 0.8,
sigma_scale = 0.6,
quant = 2,
ang_th = 22.5,
log_eps = 0,
density_th = 0.7,
n_bins = 1024,
union = FALSE,
union_min_length = 5,
union_max_distance = 5,
union_ang_th = 7,
union_use_NFA = FALSE,
union_log_eps = 0)
linesegments
plot(image)
plot(linesegments, add = TRUE, col = "red")scale = 0.1
linesegments <- image_line_segment_detector(x,
scale = scale,
sigma_scale = 0.6,
quant = 2,
ang_th = 22.5,
log_eps = 0,
density_th = 0.7,
n_bins = 1024,
union = FALSE,
union_min_length = 5,
union_max_distance = 5,
union_ang_th = 7,
union_use_NFA = FALSE,
union_log_eps = 0)
linesegments
plot(image)
plot(linesegments, add = TRUE, col = "red")quant
-default: 2 - no segments with quant 10 - quant 1: more detailed segments in tissue
quant = 1
linesegments <- image_line_segment_detector(x,
scale = 0.8,
sigma_scale = 0.6,
quant = quant,
ang_th = 22.5,
log_eps = 0,
density_th = 0.7,
n_bins = 1024,
union = FALSE,
union_min_length = 5,
union_max_distance = 5,
union_ang_th = 7,
union_use_NFA = FALSE,
union_log_eps = 0)
linesegments
plot(image)
plot(linesegments, add = TRUE, col = "red")- quant 3: single details in tissue
quant = 3
linesegments <- image_line_segment_detector(x,
scale = 0.8,
sigma_scale = 0.6,
quant = quant,
ang_th = 22.5,
log_eps = 0,
density_th = 0.7,
n_bins = 1024,
union = FALSE,
union_min_length = 5,
union_max_distance = 5,
union_ang_th = 7,
union_use_NFA = FALSE,
union_log_eps = 0)
linesegments
plot(image)
plot(linesegments, add = TRUE, col = "red")ang_th
- default: 22.5
- ang_th 1: no lines segments
- ang_th 100:
ang_th <- 100
linesegments <- image_line_segment_detector(x,
scale = 0.8,
sigma_scale = 0.6,
quant = 2,
ang_th = ang_th,
log_eps = 0,
density_th = 0.7,
n_bins = 1024,
union = FALSE,
union_min_length = 5,
union_max_distance = 5,
union_ang_th = 7,
union_use_NFA = FALSE,
union_log_eps = 0)
linesegments
plot(image)
plot(linesegments, add = TRUE, col = "red")log_eps
log_eps <- 10
linesegments <- image_line_segment_detector(x,
scale = 0.8,
sigma_scale = 0.6,
quant = 2,
ang_th = 22.5,
log_eps = log_eps,
density_th = 0.7,
n_bins = 1024,
union = FALSE,
union_min_length = 5,
union_max_distance = 5,
union_ang_th = 7,
union_use_NFA = FALSE,
union_log_eps = 0)
linesegments
plot(image)
plot(linesegments, add = TRUE, col = "red")library(pixmap)
library(magick)
imagelocation <- system.file("extdata", "chairs.pgm", package="image.LineSegmentDetector")
image <- read.pnm(file = imagelocation, cellres = 1)
x <- image@grey * 255
linesegments <- image_line_segment_detector(x,union=TRUE)
linesegments
plot(image)
plot(linesegments, add = TRUE, col = "red")image_contour_detector
contourlines <- image_contour_detector(x, Q = 2)
contourlines
plot(image)
plot(contourlines, add = TRUE, col = "red")image_canny_edge_detector
edges <- image_canny_edge_detector(x)
edges
plot(edges)